The value of a decision can be increased through analyzing the decision logic, and the outcomes. The more often a decision is taken, the more data becomes available about the results. More available data results into smarter decisions and increases the value the decision has for an organization. The research field addressing this problem is Decision mining. By conducting a literature study on the current state of Decision mining, we aim to discover the research gaps and where Decision mining can be improved upon. Our findings show that the concepts used in the Decision mining field and related fields are ambiguous and show overlap. Future research directions are discovered to increase the quality and maturity of Decision mining research. This could be achieved by focusing more on Decision mining research, a change is needed from a business process Decision mining approach to a decision focused approach.
We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Digital technologies permeate and transform organisational practices. As a society, we need means to explore the uncharted terrain that lies ahead and the desirability and consequences of possible courses of action to move forward. We investigate a design approach, called ‘future probing’, to envision and critically analyse possible futures around digital technologies. We first reconstruct our journey and describe related insights on the process, content and context level. Reflecting on the journey, we then extract a key insight revolving around the challenge for participants to link back from exploring the future to their present practice. In a first attempt at theorizing these difficulties, we see future probing as a practice that opens up adaptive space (Uhl-Bien & Arena, 2017) in which people from different backgrounds engage in dialogue about possible futures of digital technologies. We found that adaptive processes, like semi structuring, temporary decentralisation, and collaboration (Uhl-Bien & Arena, 2018) were supported by the future probing practices and seemed to create space for employees to engage in exploration. There was still a lack of compelling acts of brokering and network cohesion (Uhl-Bien & Arena, 2018). This may indicate why linking back to daily practice is challenging. We assume that organising for adaptability requires a deliberate act of connecting far future explorations with present action, and propose that besides explorative skills, ‘adaptive anticipating’ action is needed to make the connection and that linking back through near future experiments might be a way to achieve this.
